factors influencing ebidding adoption viva defence
DESCRIPTION
Viva Presentation Slide Online Electronic Reverse Auctions eRAs eBidding MalaysiaTRANSCRIPT
Analyzing Factors that Influence eBidding Utilization in Malaysian Public Sector
By
Megat Shariffudin B. Hj. Zulkifli(GM03958)
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Scope of Presentation
• Introduction• Overview of the eBidding System • Problem Statement• Research Objectives• Research Framework• Hypotheses• Methodology• Data Analysis• Findings• Summary• Policy and Practical Implications• Theoretical Implications• Limitations of the Study• Recommendations for Future Research
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Introduction Malaysian Govt. Aspire for Public Sector Reform to
achieve World-Class Government
e-Government to increase Efficiency, Inter-Agency Cooperation's and Enhanced Service Delivery
Leverage on ePerolehan - Interconnects Suppliers and Buyer via Complete End-to-End Integration Services
eBidding as new innovative G2B procurement auctions (MAMPU, 2005)
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eBidding Overview
Source: (home.eperolehan.gov.my,2009) 4
1. BuyerPosting a Product Request
2.Suppliers Bidding Against Each Other Pushing down Price
S1 S2
S3
3.Buyer Compares the Price Offers and reach Decision
5. Buyer Buys at Lowest Cost
4.Seller Lower Profit but Fast Sale
S3
Reverse Auctions An online procurement auctions performed between multiple
suppliers in real-time via the Internet produces dynamic, competitive process and downward price pressure (Jap, 2007)
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eBidding Overview
1. Acquisition time2. Price Offers3. Procurement decision -making
Conventional/Manual eBidding
- 1 to 3 months- Fixed by Suppliers- Time consuming
- 1 to 14 days- Multiple bids lower prices - Immediately (up to 7 days) ; shorter cycle time for suppliers
Source : http://www.casb.com.my, 2012
Introduced in 2006 as ePerolehan module Electronic bidding mode where the Buyer (Government) get the
Suppliers to compete interactively online to reach lowest price offer to the Buyer
Criteria:o Involve multiple MoF-registered Suppliers with single
procuring agency (Buyer)o Session is online and real-timeo Bidding session is set within a period of time (2 weeks)o Final price is derived from competitive lowest priceo Maximum of 6 sessions can be held at one time
Comparisons:-
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Problem Statements Current eBidding adoption is Low, but planned adoption by
Government is high since Launched in 2006
For example, in 2012, out of 888,866 procurement transactions, only 606 transacting units via eBidding.
Practitioners and Users agree that eBidding is a great idea for cost savings, transparency and shorter cycle time for suppliers, but actual adoption continues to lag.
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eBidding Status
Source : http://home.eperolehan.gov.my/v2/index.php/bm/mengenai-ep/statistik-sistem-ep
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Low Usage Low eBidding utilization detrimental to Government’s
aspiration for e-Government procurement reverse auctions implementation
Losses in terms of development costs, cost and time savings, service delivery efficiency and transparency
Validation issue Gaps in the body of knowledge in terms of public
sector reverse auctions adoption empirical studies Not many empirical studies conducted to ascertain
the causes of low utilization of G2B procurement auctions among the government users.
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Problem Statements
Research Objective
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To examine the User Factors and System Factors that Influence eBidding Utilization among Government Sourcing Officials in Malaysian Public Sector
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To identify the variables that may influence the adoption of eBidding by government users
To examine the effects of the variables on the adoption of eBidding by government users
To examine if some of the variables have moderating or mediating effects on the relationships established as stated in objective 2
To propose a framework to analyze the adoption of eBidding by government users
Specific Objectives
Performance Expectancy
Effort Expectancy
Social Influence
Facilitating Conditions
eBidding Adoption
System Quality
Service Quality
Information Quality
H1
H3
H5a
H6a
H2
H4
H1aH2a
H2b
H3a
H4a
H7a
H6
H5
H7
Proposed Research Model
ExperiencePersonal Innovativeness In IT
Satisfaction
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HypothesesH1: Performance expectancy is significantly related to officials’ adoption of eBidding
H2: Effort expectancy is significantly related to officials’ adoption of eBidding
H3: Social influence is significantly related to officials’ adoption of eBidding
H4: Facilitating conditions is significantly related to officials adoption of eBidding
H5: Information quality is significantly related to eBidding adoption
H6: System quality is significantly related to eBidding adoption
H7 : Service quality is significantly related to eBidding adoption
H5a : Satisfaction significantly mediates relationship between information quality and eBidding Adoption
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H6a: Satisfaction significantly mediates relationship between system quality and eBidding Adoption
H7a: Satisfaction significantly mediates relationship between service quality and eBidding Adoption
H1a: PIIT positively moderates the relationship between performance expectancy and eBidding adoption
H2a: PIIT positively moderates the relationships between effort expectancy and eBidding adoption
H2b: Experience negatively moderates the relationship between effort expectancy and eBidding adoption
H3a: Experience negatively moderates the relationship between social influences and eBidding adoption
H4a: Experience positively moderates the relationship between facilitating conditions and eBidding adoption
Continue..
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Quantitative Approach : Survey ; Cross Sectional Study
Population : 2,558 Pusat Tanggung Jawab (PTJs) ; 2,047 ePerolehan-enabled in Peninsular Malaysia
Sampling frame : 1,507 procuring officials 604 PTJs in Klang Valley and Putrajaya.
Unit of Analysis : Individual (Officials as Individual users)
Sampling Procedure : Simple Random Sampling
Research Instruments : Self-administered Questionnaires
The Research Method
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Study InstrumentsSection Variables No. of
ItemsSource
A Performance expectancy (PE)
7 Venkatesh et al., (2003)
B Effort expectancy (EE)
7 Venkatesh et al., (2003)
C Social influence (SI) 7 Venkatesh et al., (2003)
D Facilitating conditions (FC)
7 Venkatesh et al., (2003)
E System quality (SQ) 7 Delone and Mclean, (2003)
F Information quality (IQ)
7 Delone and Mclean, (2003)
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Study InstrumentsSection Variables No. of
ItemsSource
G Service quality (SVQ)
7 Delone and Mclean, (2003)
H Satisfaction 7 Delone and Mclean, (2003)
I Experience 5 Venkatesh et al., (2003)
J Personal Innovativeness in Domain of IT (PIIT)
5 Agarwal and Karahanna, (2000)
K Adoption/Use 4 Delone and McLean, (2003)
L Respondent’s profile 6 Researcher
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Feel for data
Normality
Goodness of data
ReliabilityValidity
Hypothesis testing
Appropriate statistical
(SEM, Hierarchical, Regression)
Testing model fit
RMSEA,TLC, NFI, Chi-
square, etc
Answer for research questions
Data Collection
Data Analysis
Interpretation of Results
Discussion
Data Analysis Process
Source: Sekaran, (2003)
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• SEM - test relationships among variables in the model• Multivariate analysis – show causal dependencies between
endogenous and exogenous variables (Hair et al, 2006)• Confirmatory Factor Analysis (CFA) – measure data
consistency with research model ; Factor Analysis and Path Analysis (Sekaran, 2003)• Pre-Test : Normality ; Reliability and Validity tests • Test Steps :
o Developing a Modelo Path Diagram Relationship o Structural and Measurement Modelso Proposed Model Estimationo Assessing the model Identificationo Evaluate the Goodness of Fit Criteria – TLI, RMSEA,
Chi-square, NFI, CFIo Modifying the Model – re-specification by trimming and
adding paths to achieve model fit
DATA ANALYSIS : STRUCTURAL MEASUREMENT MODELING
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DATA ANALYSIS:NORMALITY Normality test examine data from normal distribution -
examine the central tendency and dispersion
Tests for Mean, Standard Deviation, Skewness and Kurtosis.
Data must be multivariate normality to avoid biased result (Sekaran, 2003)
Data Normality if value of skewness and kurtosis = +-1 (Hisham, 2008)
From the results (Table 10), all data from constructs falls within +-1, hence normally distributed
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DATA ANALYSIS: RELIABILITY
To examine the consistency of respondents in answering the questionnaire items.
Construct reliability measure the degree to which the items were free from random error to produce consistent results (Sekaran, 2003).
Cronbach’s alpha - used in testing consistency reliability between items that is used for multipoint-scaled items Likert scale. .
Cronbach alpha value of 0.5 and higher is considered sufficient in determining reliability of the item (Sekaran, 2003).
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The results indicates all factor loadings for the study constructs are found significant and surpassed the 0.5 guideline recommended by Hair et al., (2006).
All constructs variance extracted estimate surpassed the 50 per cent. The composite reliability values are higher than 0.6 ranging from 0.82 to 0.94.
From the results (Table 11) , the constructs have adequate reliability
DATA ANALYSIS: RELIABILITY
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DATA ANALYSIS: VALIDITY Each constructs tested for discriminant validity
Discriminant validity measures whether one variable is internally correlated, unique and distinct from other variables (Tong, 2007).
A correlation value of 0.5 shows distinct, whereas a correlation value of 0.8 and higher shows a lower distinct.
The results (Table 12) all constructs are less than 0.8 indicating the presence of discriminant validity
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SUMMARY OF HYPOTHESES TESTING
Hypotheses Results
PastEmpirical Studies
H1 Supported Venkatesh et al., (2003) ; Louho et al., (2006) ; Al-Qeisi (2009)
H2 SupportedHelaiel, (2009); Rosen, (2004); Venkatesh et al., (2003) ; Park et al., (2007) ; Carlsson et al., (2006) ; Gefen and Straud, (2000)
H3 SupportedKarahanna and Straub, (1999); Rosen, (2004); Venkatesh et al., (2003) ; Wolin and Korgaonkar, (2003) ; Singh et al., (2010) ; Amin et al., (2008)
H4 Supported Hung et al., (2006) ; Venkatesh et al., (2003) ; Wu et al., (2007) ; Joshua and Koshy, (2011)
H5 Supported Delone and Mclean, (2003); Nelson et al., (2005) ; Wang (2008) ; Lee et al., (2007) ; Lin, (2006)
H6Not
supportedNone.
H7Not
supportedHalonen and Martikainen (2011) found service quality of the system is not significant in the use of an IS system
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SUMMARY OF HYPOTHESES TESTING (cont.)
H5aSupported Wixom and Todd (2005) ; Cronin et al., (1992); Cheung
and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)
H6aSupported Wixom and Todd (2005) ; Cronin et al., (1992); Cheung
and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)
H7aSupported Wixom and Todd (2005) ; Cronin et al., (1992); Cheung
and Lee, (2005) ; Kim et al., (2009) ; Liu et al., (2000) ; Cora K.L. (2009)
H1aNot
SupportedRosen A., (2004) - found PIIT did not have a moderator role between PE and behaviour intentions
H2aNot
SupportedRosen A., (2004) - found PIIT did not have a moderator role between EE and behaviour intentions
Hypotheses
ResultsPast
Empirical Studies
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Hypotheses
ResultsPast
Empirical Studies
H2b
Not Supported
None. One possible explanation is that users prior experience with similar e-auctions e.g. Lelong and eBay not affected the perception of the IS ease of use
H3a
Not Supported
None. Possible explanation is that users past experience not affected the effects of peer pressure of using IS
H4aNot
Supported
None. Users prior experience not affected the perception of availability of infrastructure/facilities supporting the system.
SUMMARY OF HYPOTHESES TESTING (cont.)
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SUMMARY• The user behavior factors (performance expectancy ;
effort expectancy; social influence & facilitating conditions) significantly related to eBidding adoption, including IQ.
• System quality and service quality are proven to be not significantly associated with eBidding adoption
• Experience and personal innovativeness in IT (PIIT) are confirmed not to exhibit moderating effects on relationships between user factors and eBidding adoption
• Satisfaction is found to have a full mediating effect on system quality and adoption and partial mediating effect on information quality with adoption and service quality with adoption of eBidding
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POLICY AND PRACTICAL IMPLICATIONS
• Improving e-procurement auctions policy with better understanding user behaviour – to incorporate PE, EE, SI, FC and users satisfaction factors in policy planning.
• Program managers and the early adopters of the eBidding should communicate the usefulness to peers about the benefits of using the eBidding
• Support eBidding use through review of policy and circulars relevant to the eBidding system e.g. mandatory use
• eBidding is reliable and productive system, can be improved by considering other attributes, such as, product specifiability, value and price.
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IMPLICATIONS FOR THEORY• Combination of determinants from various disciplines
• Validation of UTAUT theory in G2B reverse auctions setting
• Provide empirical support that the eBidding adoption is influenced by system factors mediated by user satisfactions.
• In terms of methodological implications, SEM is recommended for model testings.
• There are various benefits of SEM over other multivariate techniques. SEM can provide estimates of error variance parameters, while multivariate techniques are not able to correct measurement error.
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LIMITATIONS OF THE STUDY
•Research done only in the Putrajaya, Klang Valley and Seremban
•Sample size as most common SEM estimation procedure is MLE with minimum sample size of 150 - 200 cases (Hair et al, 2006)
•A cross sectional study but not a longitudinal study
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• Studies in other areas in the Peninsular and Sabah Sarawak.
• Further study with the inclusion of the suppliers
• Future studies in a longitudinal context
• Incorporate determinants from reverse auctions attributes (i.e. value, product specifications, competitiveness)
RECOMMENDATIONS FOR FUTURE RESEARCH
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THANK YOU
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Notes
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Problems Statemen
t& Study
Objectives
Global eG Development
Malaysia’s eGovernment
ePerolehan
Online Reverse Auctions
Overview of eBidding
Overview of Established theories of User AcceptanceTAM1,Diffusion of Innovation
UTAUTIS Success Model
PIIT
Theoretical and
Empirical Studies User
Factors-> Behavioral Intention
System Quality Factors–> Behavioral Intention
Personal Innovativeness on IT–> Behavioral IntentionModels Comparisons
Integrative Model Proposed
Theoretical and
Empirical Studies User &
System Quality Factors–> Adoption Behavior
Mediator role of User Satisfaction
Moderator Factors on the User Factors -> Adoption Behavior
LiteratureLiterature
Review
The study and the model proposed will be based on:
The Relevant Theories
Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)An Information System (IS) framework for assessing an individual’s intention to use an IS technologyInformation System Success Model (Delone and Mclean, 2003)A system success can be evaluated in terms of information, system, and service quality; these characteristics affect the subsequent use or intention to use and user satisfactionPersonal Innovativeness in Information Technology (Agarwal and Prasad, 1998)Domain-specific individual trait which reflects the willingness of a person to try out a new information technology
1
2
3
Theoretical Models
Moderator Model of Personal Innovativeness in Information Technology (PIIT) (Agarwal & Prasad, 1998)
Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)
IS Updated Success Model (Delone and Mclean, 2003)
No Construct Factor loading
Average Variance Extracted
(AVE)
Composite Reliability
Cronbach's Alpha
1.Performance Expectancy (PE) .75 .92 .929
PE1 .888 PE2 .949 PE3 .831 PE4 .798
2. Effort Expectancy (EE) .79 .94 .932
EE4 .948 EE5 .974 EE6 .897 EE7 .703
3. Social Influence (SI)
SI 1 .873 .7 .9 .908 SI 3 .879 SI 5 .686 SI 7 .888
4.Facilitating Conditions (FC)
.886
FC2 .787 .66 .88 FC 4 .763 FC 5 .733 FC 7 .95 3
7
5. Information Quality (IQ) .891
IQ2 .822 .7 .9 IQ4 .803 IQ5 .735 IQ6 .957
6. System Quality (SYQ).72 .91
.891
SYQ3 .568 SYQ5 .906 SYQ6 .899 SYQ7 .969
7. Service Quality (SVQ) .65 .88.878
SVQ1 .803 SVQ3 .883 SVQ5 .78 SVQ6 .7548. Actual Use (USE) .67 .89 .892 USE1 .756 USE2 .934 USE3 .911 USE 4 .6389. Satisfaction .53 .82 .818 Satisfaction1 .76 Satisfaction2 .66 Satisfaction3 .69 Satisfaction4 .80
No Construct Factor loading
Average Variance Extracted
(AVE)
Composite Reliability
Cronbach's Alpha
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Variable X1 X2 X3 X4 X5 X6 X7 X8 X9
PE (X1) 1
Adoption/Use (X2) .660** 1
EE (X3) .643** .771** 1
SI (X4) .462** .809** .605** 1
FC (X5) .129 .406** .242** .622** 1
SQ (X6) .683** .542** .425** .379** .039 1
IQ (X7) .423** .698** .513** .798** .785** .328** 1
SVQ (X8) .437** .721** .543** .723** .561** .358** .675** 1
Satisfaction (X9) .710** .815** .696** .570** .191* .557** .482** .522**1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
DATA ANALYSIS: VALIDITY
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